import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_SCREAMING_SNAKE_CASE 		=      False
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):
   pass





@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""				)
    # remove text_unet
    pipe.remove_unused_weights()
    pipe.to(lowerCamelCase_				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      """A painting of a squirrel eating a burger """
    UpperCamelCase			  =      torch.manual_seed(0				)
    UpperCamelCase			  =      pipe(
        prompt=lowerCamelCase_   ,   generator=lowerCamelCase_   ,   guidance_scale=7.5   ,   num_inference_steps=2   ,   output_type="""numpy"""				).images

    with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(lowerCamelCase_				)
     UpperCamelCase			  =      VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase_				)
    pipe.to(lowerCamelCase_				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      generator.manual_seed(0				)
    UpperCamelCase			  =      pipe(
        prompt=lowerCamelCase_   ,   generator=lowerCamelCase_   ,   guidance_scale=7.5   ,   num_inference_steps=2   ,   output_type="""numpy"""				).images

    assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
        """shi-labs/versatile-diffusion"""   ,   torch_dtype=torch.floataa				)
    pipe.to(lowerCamelCase_				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      """A painting of a squirrel eating a burger """
    UpperCamelCase			  =      torch.manual_seed(0				)
    UpperCamelCase			  =      pipe(
        prompt=lowerCamelCase_   ,   generator=lowerCamelCase_   ,   guidance_scale=7.5   ,   num_inference_steps=50   ,   output_type="""numpy"""				).images

    UpperCamelCase			  =      image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    UpperCamelCase			  =      np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
